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Here’s how ChatGPT’s upcoming ‘Study Together’ tool could enhance learning (APK teardown)

Calvin Wankhede / Android Authority TL;DR OpenAI is working on a dedicated “Study Together” mode to help users grasp concepts better. Study Together is likely to help break down concepts into simpler terms and follow up with quizzes for more engaged learning. It is being tested with both free and paid users, suggesting non-paying users might also have access when it launches. AI tools, such as ChatGPT, have accelerated learning by making concepts much easier to find and summarize. Now, OpenA

Apple’s machine learning framework is getting support for NVIDIA’s CUDA platform

Apple’s MLX machine learning framework, originally designed for Apple Silicon, is getting a CUDA backend, which is a pretty big deal. Here’s why. The work is being led by developer @zcbenz on GitHub (via AppleInsider), who started prototyping CUDA support a few months ago. Since then, he split the project into smaller pieces, and gradually merged them into Apple’s MLX’s main branch. The backend is still a work in progress, but several core operations, like matrix multiplication, softmax, reduc

Show HN: I built this to talk Danish to my girlfriend – works with any language

Ever wanted to text your foreign partner or chat with international friends but felt held back by missing vocabulary? Don't let language barriers stop you from connecting. No sign-ups, no subscriptions. Just open the app and start learning. Perfect for spontaneous moments when you need to express something important. Every correction is saved. Review your mistakes, practice pronunciation, and track your progress as you naturally improve. Hear how every correction should sound with high-qualit

AI text-to-speech programs could “unlearn” how to imitate certain people

AI companies generally keep a tight grip on their models to discourage misuse. For example, if you ask ChatGPT to give you someone’s phone number or instructions for doing something illegal, it will likely just tell you it cannot help. However, as many examples over time have shown, clever prompt engineering or model fine-tuning can sometimes get these models to say things they otherwise wouldn’t. The unwanted information may still be hiding somewhere inside the model so that it can be accessed

6 free Android apps I use to learn something new every day

Megan Ellis / Android Authority I’ve had a lifelong love of learning, to the point where I used to read encyclopedias and dictionaries as a child, along with a variety of non-fiction books around specific topics. This love of learning hasn’t dampened as an adult, as I frequently find myself in Wikipedia rabbit holes, and my YouTube Watch Later list is filled with topics around science, history, psychology, and other topics I want to learn about. The difference, however, is that I have a lot le

Claude can now connect to learning apps like Canvas, Panopto and Wiley

At the start of April, Anthropic released Learning mode, a feature that changed how Claude would interact with users. With the tool enabled, the chatbot would attempt to guide students to a solution rather than providing them with an answer outright. The release of Learning mode and Claude for Education was the start of a major push by Anthropic to work with universities and colleges globally. Today, the company is upgrading Claude for Education with the addition of integrations to three popula

The era of exploration

Large language models are the unintended byproduct of about three decades worth of freely accessible human text online. Ilya Sutskever compared this reservoir of information to fossil fuel, abundant but ultimately finite. Some studies suggest that, at current token‑consumption rates, frontier labs could exhaust the highest‑quality English web text well before the decade ends. Even if those projections prove overly pessimistic, one fact is clear: today’s models consume data far faster than humans

ChatGPT Glossary: 53 AI Terms Everyone Should Know

AI is everywhere. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, AI is completely taking over the internet. With AI, you can get instant answers to pretty much any question. It can feel like talking to someone who has a Ph.D. in everything. But that aspect of AI chatbots is only one part of the AI landscape. Sure, having ChatGPT help do your homework or having Midjourney create fascinating images of mechs based on country of origin is co

The Era of Exploration

Large language models are the unintended byproduct of about three decades worth of freely accessible human text online. Ilya Sutskever compared this reservoir of information to fossil fuel, abundant but ultimately finite. Some studies suggest that, at current token‑consumption rates, frontier labs could exhaust the highest‑quality English web text well before the decade ends. Even if those projections prove overly pessimistic, one fact is clear: today’s models consume data far faster than humans

I don't think AGI is right around the corner

“Things take longer to happen than you think they will, and then they happen faster than you thought they could.” - Rudiger Dornbusch I’ve had a lot of discussions on my podcast where we haggle out timelines to AGI. Some guests think it’s 20 years away - others 2 years. Here’s where my thoughts stand as of June 2025. Continual learning Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the internet.

Just Ask for Generalization (2021)

Generalizing to what you want may be easier than optimizing directly for what you want. We might even ask for "consciousness". This blog post outlines a key engineering principle I’ve come to believe strongly in for building general AI systems with deep learning. This principle guides my present-day research tastes and day-to-day design choices in building large-scale, general-purpose ML systems. Discoveries around Neural Scaling Laws, unsupervised pretraining on Internet-scale datasets, and o

Reinforcement learning, explained with a minimum of math and jargon

It’s Agent Week at Understanding AI! This week I’m going to publish a series of articles explaining the most important AI trend of 2025: agents! Today is a deep dive into reinforcement learning, the training technique that made agentic models like Claude 3.5 Sonnet and o3 possible. Today’s article is available for free, but some articles in the series—including tomorrow’s article on MCP and tool use—will be for paying subscribers only. I’m offering a 20 percent discount on annual subscriptions

The Personalized Learning Revolution: An EdTech Insider’s Perspective

Back in the 90s, when I was in school, education was like a uniform everyone had to wear—the same textbooks, the same blackboard, and the same hurried lessons for all. If you fell behind, your only lifeline was to awkwardly raise your hand in the middle of class or spend hours in the library after school, rifling through reference books. Fast forward 30 years, and it’s fascinating how far we’ve come. Today, thanks to AI/ML, we have adaptive learning systems—tailored to each student based on thei

Show HN: PILF, The ultimate solution to catastrophic oblivion on AI models

Technical Notes: PILF (Predictive Integrity Learning Framework) Document Version: 3.0 Core Concept: A cognitive learning framework designed to transform fixed hyperparameters (like learning rate, model capacity) into dynamic policies driven in real-time by the intrinsic "surprise" ( Surprise ) of data. It is essentially an adaptive hyperparameter scheduling algorithm that allows a model to autonomously decide "how much to learn" and "with what capacity to learn" based on the value of the learn

Beyond static AI: MIT’s new framework lets models teach themselves

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Researchers at MIT have developed a framework called Self-Adapting Language Models (SEAL) that enables large language models (LLMs) to continuously learn and adapt by updating their own internal parameters. SEAL teaches an LLM to generate its own training data and update instructions, allowing it to permanently absorb new knowledge and lea

Tech giants unleash AI on weather forecasts: are they any good?

A wave of machine-learning weather models have been unleashed by some of the very biggest businesses on the planet. These challenge the orthodoxy of traditional physics-based computer forecasts that have been incrementally developed and improved over many decades. But are the machine learning models any good? The weather is a national obsession for us Brits, and it is no wonder given the huge changes that are seen and felt from one day to the next.

Foundations of Computer Vision (2024)

Foundations of Computer Vision Preface Dedicated to all the pixels. About this Book This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well. Our initial goal was to write a large book that provided a g

Tiny-diffusion: A minimal implementation of probabilistic diffusion models

A minimal PyTorch implementation of probabilistic diffusion models for 2D datasets. Get started by running python ddpm.py -h to explore the available options for training. Forward process A visualization of the forward diffusion process being applied to a dataset of one thousand 2D points. Note that the dinosaur is not a single training example, it represents each 2D point in the dataset. Reverse process This illustration shows how the reverse process recovers the distribution of the trainin

Foundations of Computer Vision

Foundations of Computer Vision Preface Dedicated to all the pixels. About this Book This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well. Our initial goal was to write a large book that provided a g

Q-learning is not yet scalable

Does RL scale? Over the past few years, we've seen that next-token prediction scales, denoising diffusion scales, contrastive learning scales, and so on, all the way to the point where we can train models with billions of parameters with a scalable objective that can eat up as much data as we can throw at it. Then, what about reinforcement learning (RL)? Does RL also scale like all the other objectives? Apparently, it does. In 2016, RL achieved superhuman-level performance in games like Go and C

ChatGPT Glossary: 52 AI Terms Everyone Should Know

AI is now a part of our everyday lives. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, AI is completely taking over the internet. With AI, you can get instant answers to pretty much any question. It can feel like talking to someone who has a Ph.D. in everything. But that aspect of AI chatbots is only one part of the AI landscape. Sure, having ChatGPT help do your homework or having Midjourney create fascinating images of mechs based on c

Computing’s Top 30: Tejas Padliya

Balancing technology and social good is tricky; doing it well requires both practical expertise and a compelling vision. For software engineer Tejas Padliya, alchemizing the two is the driving force in his work. Padliya’s expertise is in AI and digital health technologies. His vision? For AI to be both a tool and a catalyst for equitable, data-driven healthcare transformation. In conference presentations and elsewhere, Padliya conveys two powerful messages: Technology is about changing lives,

A Deep Learning Alternative Can Help AI Agents Gameplay the Real World

A new machine learning approach that draws inspiration from the way the human brain seems to model and learn about the world has proven capable of mastering a number of simple video games with impressive efficiency. The new system, called Axiom, offers an alternative to the artificial neural networks that are dominant in modern AI. Axiom, developed by a software company called Verse AI, is equipped with prior knowledge about the way objects physically interact with each other in the game world.